In this Data Science and Machine Learning Speaker Series, Heather Harris, PhD, Founder and Principal Data Scientist at Herkimer Consulting, offers an in-depth exploration of missing data mechanisms, data screening processes, and an introduction to widely-used imputation methods. Addressing missing data is a critical challenge in model development, particularly in AI, as the way incomplete data is handled can significantly affect model accuracy, efficiency, and decision-making. Dr. Harris emphasizes the practical gap between theoretical approaches and real-world applications, highlighting how an inadequate understanding of missing data can distort results and undermine the integrity of AI models. This primer underscores the importance of robust strategies for managing incomplete data to ensure reliable, data-driven outcomes.